# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for np_utils."""

import numpy as np
import tensorflow.compat.v2 as tf

from keras.utils import np_utils


class TestNPUtils(tf.test.TestCase):
    def test_to_categorical(self):
        num_classes = 5
        shapes = [(1,), (3,), (4, 3), (5, 4, 3), (3, 1), (3, 2, 1)]
        expected_shapes = [
            (1, num_classes),
            (3, num_classes),
            (4, 3, num_classes),
            (5, 4, 3, num_classes),
            (3, num_classes),
            (3, 2, num_classes),
        ]
        labels = [np.random.randint(0, num_classes, shape) for shape in shapes]
        one_hots = [
            np_utils.to_categorical(label, num_classes) for label in labels
        ]
        for label, one_hot, expected_shape in zip(
            labels, one_hots, expected_shapes
        ):
            # Check shape
            self.assertEqual(one_hot.shape, expected_shape)
            # Make sure there is only one 1 in a row
            self.assertTrue(np.all(one_hot.sum(axis=-1) == 1))
            # Get original labels back from one hots
            self.assertTrue(
                np.all(np.argmax(one_hot, -1).reshape(label.shape) == label)
            )


if __name__ == "__main__":
    tf.test.main()
